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Competence-based recommender systems: a systematic literature review

机译:基于能力的推荐系统:系统的文献综述

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Competence-based learning is increasingly widespread in many institutions since it provides flexibility, facilitates the self-learning and brings the academic and professional worlds closer together. Thus, the competence-based recommender systems emerged taking the advantages of competences to offer suggestions (performance of a learning experience, assistance of an expert or recommendation of a learning resource) to the user (learner or instructor). The objective of this work is to conduct a new Systematic Literature Review (SLR) concerning competence-based recommender systems to analyse in relation to their nature and assessment of competences an others key factors that provide more flexible and exhaustive recommendations. To do so, a SLR research methodology was followed in which 25 competence-based recommender systems related to learning or instruction environments were classified according to multiple criteria. We evaluate the role of competences in these proposals and enumerate the emerging challenges. Also a critical analysis of current proposals is carried out to determine their strengths and weakness. Finally, future research paths to be explored are grouped around two main axes closely interlinked; first about the typical challenges related to recommender systems and second, concerning ambitious emerging challenges.
机译:基于能力的学习在许多机构中越来越普遍,因为它提供了灵活性,促进了自我学习并使学术界和专业界更加紧密地联系在一起。因此,出现了基于能力的推荐器系统,其利用了能力的优势来向用户(学习者或指导者)提供建议(学习经验的表现,专家的帮助或学习资源的推荐)。这项工作的目的是针对基于胜任力的推荐系统进行新的系统文献综述(SLR),以分析其性质并评估胜任力的其他关键因素,从而提供更为灵活和详尽的推荐。为此,采用了SLR研究方法,其中根据多个标准对25个与学习或指导环境相关的基于能力的推荐系统进行了分类。我们评估了能力在这些建议中的作用,并列举了新出现的挑战。还对当前建议进行了批判性分析,以确定其优势和劣势。最后,将要探索的未来研究路径围绕紧密联系的两个主轴进行分组。首先是与推荐系统相关的典型挑战,其次是雄心勃勃的新兴挑战。

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